Sr. Machine Learning Engineer, PXT EXR-Ops Tech

AI in this role

Senior Machine Learning Engineer driving the design, architecture, and delivery of large-scale ML systems and generative AI services.

ragfine-tuningml-opsai-safetymachine-learninggenerative-aillmsmlopsdistributed-systems

We are looking for a Senior Machine Learning Engineer who will serve as a technical leader, driving the design and delivery of complex, large-scale ML systems that have significant business impact. In this role, you will define the ML architecture and technical strategy for your team, influence cross-organizational ML platform decisions, and raise the bar for ML engineering excellence. You will build super-intelligent AI systems, lead the development of production-grade AI services including generative AI and LLMs, and mentor a team of ML engineers. This is a high-impact role requiring deep technical expertise, strong judgment, and the ability to operate in ambiguous problem spaces.

Key job responsibilities
• Define and drive the ML technical strategy and architecture for your team and adjacent teams
• Design and lead the implementation of complex, multi-model ML systems at Amazon scale
• Architect ML platforms and frameworks that enable rapid experimentation and deployment across the organization
• Lead the design of real-time and batch ML serving infrastructure with high availability and low latency
• Drive adoption of ML best practices including MLOps, model governance, responsible AI, and cost optimization
• Evaluate and introduce new ML technologies, frameworks, and methodologies (e.g., foundation models, RAG, fine-tuning, RLHF)
• Lead cross-functional initiatives spanning science, engineering, product, and business teams
• Influence org-wide technical decisions through design reviews, architecture reviews, and technical writing (design documents, white papers)
• Mentor and develop L4 and L5 ML engineers; raise the hiring bar as a bar raiser or interviewer
• Drive operational excellence: define SLAs, build dashboards, automate incident response, and reduce operational burden

Basic qualifications

- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- Experience in technical support, or experience with training and deploying machine learning systems to solve large-scale optimizations
- Experience building large-scale machine learning and AI solutions at Internet scale
- Experience in creating and managing complex, cross-team project plans
- 4+ years of distributed systems experience, or Bachelor's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience building complex software systems that have been successfully delivered to customers

Preferred qualifications

- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
- Experience leading engineering discussions around technology decisions and strategy related to a product
- • Experience defining ML platform strategy and building reusable ML infrastructure

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Bellevue - 168,100.00 - 227,400.00 USD annually

How we rate this

Sr. Machine Learning Engineer, PXT EXR-Ops Tech at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Sr. Machine Learning Engineer, PXT EXR-Ops Tech is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $168k-$227k a year.
The role is based in US, WA, Bellevue.
The job was posted on September 22, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGFine TuningML OpsAI SafetyMachine LearningGenerative AILLMsMlops

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
How do you think about the risk of an AI system in this kind of role failing silently?
Tell me about a project where machine learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, Fine Tuning, ML Ops, AI Safety, and Machine Learning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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